Skill mismatches in the EU: Immigrants vs. Natives Sandra ... · effective work experience in Denmark. Thus, they successfully assimilate. As for the returns of years of overeducation,

WP3/08 SEARCH WORKING PAPER

Skill mismatches in the EU: Immigrants vs. Natives

Sandra Nieto, Alessia Matano, Raúl Ramos

January 2013

SKILL MISMATCHES IN THE EU:

IMMIGRANTS vs. NATIVES1

Sandra Nieto, Alessia Matano, Raúl Ramos

AQR‐IREA (Universitat de Barcelona)

Abstract:

The situation of immigrants within their host countries’ labour markets is generally worse than

the situation of natives. We focus our interest in the analysis of the differences in skill

mismatches between immigrants and natives in EU countries. We use microdata from the

Adult Education Survey (AES) carried out in 2007. This dataset allows us to analyse the

incidence of different types of skill mismatches (vertical and horizontal) among native and

immigrant workers. We do not find any significant difference in the probability of having

horizontal mismatch between natives and immigrants once individual characteristics are

controlled for. However, we find that immigrants are more likely to be overeducated than

natives, and that this effect is higher for immigrants coming from non‐EU countries than for

those coming from other EU countries. Nonetheless, the pace of the assimilation process in

the host country is faster for the first group. By means of the Yun decomposition, we also find

that immigrants from the EU have a higher probability of being overeducated than natives

because they have worse observable characteristics which influence positively the probability

of overeducation, whereas results for immigrants from non‐EU countries suggest the opposite:

the gap is explained by differences in the returns to observable characteristics. This result

suggests that immigrants from non‐UE countries have a limited transferability of their human

capital that pushes their situation of overeducation in the host country.

Keywords: Immigration, overeducation, assimilation.

JEL Codes: J61, J24

1 We make use of microdata from the European Commission, Eurostat, AES 2007 database made available by Eurostat under contract AES/2012/06. Eurostat has no responsibility for the results and conclusions reported here.

Skill mismatches in the EU: Immigrants vs Natives SEARCH WP3/08

1

1. INTRODUCTION, BACKGROUND AND OBJECTIVES

Human capital is one of the key factors in the determination of most of labour market

outcomes (Card, 1999; Psacharopoulos and Patrinos, 2004). Consistent with this perspective,

the analysis of the situation of immigrants within their host countries’ labour markets has also

focused on their human capital. In particular, the two main empirical results from this

literature —the presence of a significant initial wage gap relative to native‐born workers and

the rapid wage growth from the moment of arrival—can basically be explained by their human

capital. Further, human capital partially explains most differences between immigrants and

natives in terms of participation in labour market or job quality, among others. Thus, the

disadvantage experienced by immigrants when they arrive in a new country can generally be

attributed to the limited transferability of the human capital they have acquired in their home

country. The reason may lie in the lower quality of the educational system there or in the

different cultural background. Whatever the case, the relevant fact is that newly arrived

immigrants seem to lack human capital adequate to the needs of the host country’s labour

market (Chiswick, 1978; Chiswick and Miller, 1985, 2009; Friedberg, 2000). Moreover, the

explanatory factor behind the rapid growth in immigrant labour market outcomes over time,

especially in wages, can be found in the accumulation of different types of human capital in

the host country, which is particularly significant in the first years of residence in the host

country (i.e, command of the host country language). It is also noteworthy that this rapid

growth in labour market outcomes generally leads to assimilation with the native population

(Chiswick, 1978; Baker and Benjamin, 1994; Chiswick and Miller, 1995 and Bell, 1997, among

others).

Within this literature, recent studies have focused on the role played by educational (or

vertical) mismatch and more specifically, on the level of overeducation. Although an extensive

body of research has analysed overeducation2 since the seminal contributions of Freeman

2 Surveys by Hartog (2000), Rubb (2003) and McGuiness (2006) have summarised the main findings of this literature.


2

(1976) and Duncan and Hoffman (1981), only a few recent studies have considered differences

between natives and immigrants in terms of skill mismatches3.

Overeducation is usually defined as the situation where workers have greater educational skills

than their jobs require (Rumberger, 1981). The idea underpinning this new literature is thus

that the imperfect portability of human capital acquired in origin countries forces immigrants

to accept jobs requiring lower qualifications than those acquired in their country, making them

formally overeducated workers4. The main outcomes of these recent studies can be summed

up in two empirical regularities. Firstly, there is a greater incidence of overeducation among

immigrants than there is among the native population. And secondly, immigrant workers

succeed in reducing the difference in overeducation with regards to the native population as

their stay in the new country is prolonged, i.e. the phenomenon of assimilation takes place in

overeducation (in a similar way to the one found in earnings assimilation).

The literature on immigrant assimilation started with Chiswick (1978) who explained the lower

marginal returns of immigrant human capital in the USA by the limited portability of their

human capital. The results obtained for other economies confirm the differences between

natives and immigrants in terms of the remuneration of their human capital, and they also find

the existence of assimilation process (Chiswick and Miller, 1995, for Australia; Baker and

Benjamin, 1994, for Canada; Bell, 1997, for the UK; Schmidt, 1992, and Constant and Massey,

2003, for Germany, and Longva and Raaum, 2003, for Norway). Shields and Wheatley Price

(1998) and Friedberg (2000) obtained also interesting results separating the education

acquired by immigrants in their country of origin from their studies conducted in the country

of destination. They find that human capital imported from culturally distant countries

receives a lower remuneration than that acquired in the country of destination, and it differs

depending on the characteristics of the origin country. Thus, the greater the distance in terms

of language, culture, and economic development, the less portable the human capital acquired

abroad becomes and the greater the initial inequality in the job market in comparison with

members of the native population. However, Duleep and Regets (1997) also found that the

3 See for instance, Piracha and Vadean (2012); Dustman and Glitz (2011) and Leuven and Oosterbeek (2011) 4 Possible differences in the quality of the different educational systems limit the comparison of native and immigrants workers. Nevertheless, many other factors (including an incomplete command of the language, qualifications not being recognised and studies adapted to the new labour market) reduce the expected productivity of hiring immigrants leading them to accept lower‐paid jobs.


3

immigrants with lower portability of their human capital present a higher speed of

assimilation.

Other interesting results were found when introducing overeducation into the analysis of the

differences between natives and immigrants. Most of the literature concludes that immigrants

have a higher rate of overeducation than natives (Chiswick and Miller, 2010). For instance,

using data from Australia, Kler (2006) and Green et al. (2007) found that the incidence of

overeducation is higher among immigrants from non‐English‐speaking countries, who show

lower returns for overeducation. In the case of the United Kingdom, Lindley and Lenton (2006)

found a higher incidence of overeducation not just among immigrants but also for non‐white

members of the native‐born population. Using data from United States, Chiswick and Miller

(2008) claim that the educational mismatch explains almost two thirds of the differences in

human capital returns between native and immigrants.

In the study of the incidence of overeducation on immigrants, other results concerning the

degree of transferability of human capital acquired in the origin country and the process of

assimilation are also interesting. In particular, Chiswick and Miller (2007) found that the

greater the work experience in the country of origin, the greater the probability of

overeducation in the United States, which indicates low transferability not just of schooling but

also of work experience acquired in origin. Sanromá et. al (2008) found that immigrants living

in Spain accumulate knowledge and experience that are perfectly adapted to the local labour

market, thus making for an easier assimilation process that reduces the intensity of over‐

education. However, the pace of assimilation is notably slow, so that around fifteen years of

living in Spain would be necessary to eliminate the educational mismatch, and it differs

depending on the origin country. Using data from New Zealand, Poot and Stillman (2010) also

concluded that it was relevant to control for origin heterogeneity when analysing the pace of

assimilation of immigrants in terms of overeducation. Last, Nielsen (2007) obtained that

overeducation in Denmark affects immigrants with studies from abroad more than it does for

natives and immigrants who have studied in Denmark. According to this author, this fact

reveals the partial portability of human capital acquired in origin. Furthermore, immigrants

with studies acquired in their own country reduce their overeducation as they increase their

effective work experience in Denmark. Thus, they successfully assimilate. As for the returns of

years of overeducation, this is lowest for immigrants with studies from abroad, followed by

immigrants with Danish qualifications, and is the highest for the native‐born population.


4

On the other hand, there are some studies that have not found any evidence of a successful

assimilation process by immigrants in the host country. Dell’Aringa and Pagani (2010) found

that the “catch‐up” by foreigners in Italy seems unachievable, even once they have adapted

their skills to the host country’s labour market. Comparing data from 25 countries, the OECD

(2007) obtained similar results in most of the countries when disaggregating results for men

and women. A similar conclusion is found by Aleksynska and Tritah (2011) when analysing data

from the European Social Survey for 22 European countries for the period 2002‐2009.

Most of these papers consider vertical mismatch, i.e. mismatch between worker’s educational

level and the one required for their job, as an indicator of skill mismatch. However, there are

other indicators of skill mismatch that have not been used until now in the analysis of

immigrants. Horizontal mismatch measures the degree of adjustment between the workers’

educational field and the one required for their job5.

With the purpose of analysing the role played by these two components of skill mismatches,

we use a database which allows us to measure both vertical and horizontal mismatches.

Indeed, to the best of our knowledge, there are no previous studies that have analysed both

types of skill mismatches separately for natives and immigrants using homogeneous

information for a wide group of European Union countries. Taking this into account, the aim of

this paper is twofold. First, we examine the determinants of being in a situation of vertical or

horizontal mismatch focusing on natives and immigrants from EU countries and from non‐EU

countries and we analyse whether there is assimilation or not. Second, we try to identify the

factor behind the observed differences in the probability of being mismatched between

natives and both types of immigrants.

The rest of the paper is organized as follows. Section 2 describes the database used and

defines the variables of interest. Section 3 shows descriptive evidence of the incidence of

vertical and horizontal mismatches between natives and immigrants, focusing also in the

analysis of the assimilation process of immigrants. Section 4 explains the applied methodology

and shows the results. Last, section 5 summarises the findings of previous sections and point

out the main policy conclusions of the analysis.

5 For instance, Robst (2007) and Wolbers (2003) use this measure as indicator of skill mismatch.


5

2. DATA SOURCES AND VARIABLES DEFINITION

2.1. Adult Education Survey

In order to achieve our objectives, we use microdata from the Adult Education Survey (AES)

provided by Eurostat. It is a survey addressed to private households with members between

25 and 64 years old. The survey has been carried out in 29 countries between 2005 and 2008

and the reference year is set at 2007. The main objective of the survey is to study lifelong

learning, that is, those training and learning activities that the adult population performs with

the objective of improving or extending their knowledge, skills and competences, from a

personal, civil, social or work‐related perspective.

This database is particularly appropriate for our analysis because, as far as we know, is the only

one that allows us to measure both vertical and horizontal mismatch in a homogeneous way

for a wide set of European Union countries and to make comparisons between immigrant

(from EU countries and from non‐EU countries) and native workers.

As we focus our interest on immigrants living in EU countries, we only consider those countries

where immigration is a relevant phenomenon (more than 4% of total population). Thus, as we

can see in Figure 1, we do not consider Bulgaria, Poland, Romania and the Slovak Republic. We

also have excluded from the analysis Hungary and the Netherlands because immigrant

population in the Adult Education Survey is clearly underrepresented when compared with

aggregate data from Eurostat. We also have to exclude Finland, Italy and the United Kingdom

from the analysis because these countries do not include in their national surveys some

relevant information for our analysis (in particular, immigrants’ years of residence in the host

country). So, after these restrictions, we consider in our analysis the following 15 European

Union countries: Austria, Belgium, Cyprus, Czech Republic, Germany, Denmark, Estonia, Spain,

France, Greece, Latvia, Lithuania, Portugal, Sweden and Slovenia.

We restrict our analysis to men and women employed at the time of the survey with valid

information about their occupation and level and field of education. We exclude from the

analysis individuals below the ISCED3 educational level. The reason to do it is because the


6

variable field of education is only defined for individuals with educational levels higher than

ISCED2. The final sample consists of 28409 native born and 2492 immigrants, of which 984

come from European Union countries and 1598 come from non‐ European Union countries.

FIGURE 1

The variables used in our analysis are related to personal and job characteristics. As for

personal characteristics, we use information related to gender, age, nationality, years of

residence in the host country, number of members of the household, children at home, level

and type of education and participation in non‐formal education activities during the last 12

months. As for job characteristics, we consider information about tenure in the current firm,

type of contract (permanent or not), part‐time job, the economic activity of the firm, and the

size of the firm. Last, we consider information about the country of residence. Descriptive

statistics for these variables are shown in Table A.1 of the Annex.

2.2. Measuring skill mismatches

Three different methods have been proposed in the literature to measure vertical mismatch:

objective, subjective and statistical method (in terms of the mean and the mode). Each

procedure has its own advantages and weaknesses6. As a consequence, the use of one or other

method usually depends on the nature of the data available.

The objective method is based on “dictionaries” of jobs, compiled by job analysts who

determine what level and type of education workers should have in order to perform a certain

job. A person is then overeducated if their level of education is higher than the level the

analysts define to be ideal for the occupation. The subjective method takes into account the

perception of the workers to determine the educational mismatch. Last, the version of the

statistical method based on the mean (Verdugo and Verdugo, 1989) considers that workers are

overeducated if they have more years of education than the mean of the years of education

(plus one standard deviation) of the workers in that occupation. Nevertheless, Kiker et al.

6 For a discussion, see Hartog (2000).


7

(1997) propose the use of the mode instead of the mean; so they consider as overeducated a

person who has more years of education than the mode of years of education in the job they

perform.

As for horizontal mismatch, most studies have applied similar methods to the ones used to

analyse vertical mismatch. In particular, they use similar approaches but using the variable

“field of education” instead of “years of education”. In this paper, we will use the statistical

method in terms of the mode for two reasons. First, we cannot use the objective method

because, unfortunately, this kind of indicator is not available for most countries, as massive

efforts will be needed to build these dictionaries, which can easily become obsolete due to

occupational change. We can neither use the subjective method because the Adult Education

Survey does not provide this information. So, we measure vertical and horizontal mismatch

using the statistical method based on the mode the Adult Education Survey provides the

needed information: occupations, educational levels and fields of education. It is worth

mentioning that as we are working with immigrants from countries with heterogeneous

educational systems, we measure vertical mismatches considering the level of education

instead of schooling years. With this way of proceeding, we expect to minimize potential

measurement errors derived from the comparison of very heterogenous educational systems.

Taking into account these previous considerations, we define both types of mismatches as

follows: workers will have vertical mismatch (overeducation) if their level of education is

higher than the mode of the workers’ level of education within each occupation whereas

workers will have horizontal mismatch if their field or type of education is different than the

mode of the workers’ field of education within each occupation.

3. DESCRIPTIVE EVIDENCE

In this section, we show a descriptive analysis on the differences between natives and

immigrants regarding horizontal and vertical skill mismatches. The percentage of natives,

immigrants from EU countries and immigrants from non‐EU countries that suffer vertical and

horizontal mismatch are shown in figures 2 and 3, respectively. Some relevant results can be

identified from these figures. First, it is worth noting that the percentages of horizontal

mismatch are higher in all groups than percentages of vertical mismatch (40‐45 versus 25‐35).


8

Second, figure 2 also shows that 25% of natives are overeducated whereas this percentage is

31% for immigrants from EU countries and 35% for immigrants from other countries.

Nevertheless, in figure 3 we can see that the percentage of horizontal mismatch for natives

and immigrants from EU countries is around 40% for both groups whilst for immigrants from

countries outside EU is higher, 45%. Although the incidence of horizontal mismatch is higher

than vertical mismatch for all groups, we observe more differences between natives and

immigrants in the incidence of vertical mismatch.

FIGURES 2 and 3

Focusing now our interest only in the immigrant population, we can see some interesting

differences depending on the years of residence in their host country. Figures 4 and 5 show,

respectively, the percentage of immigrant workers with vertical and horizontal mismatch by

years of residence in the host country. We can see in figure 5 that the incidence of horizontal

mismatch decreases for both groups of immigrants as their years of residence increase. This

result could be interpreted as evidence of immigrant assimilation. Some different results can

be observed, however, in relation to vertical mismatch (Figure 4). Regarding immigrants from

countries outside the EU, the incidence of overeducation also reduces as the years of

residence of these immigrants increase. However, such behaviour is not observed for

immigrants from EU countries. Immigrants residing less than 2 years in the host country

present a lower percentage of overeducation than immigrants residing between 3 to 5 years.

In this case, it seems that the assimilation process in the first 5 years in the host country is not

as clear for immigrants from EU countries than for the others.

FIGURES 4 and 5

However, the descriptive analysis carried out in this section does not consider the effect of the

characteristics of the individuals on differences in overeducation. This aspect is considered in

the following section.


9

4. METHODOLOGY AND RESULTS

In order to know whether there are differences in the probability of being overeducated and in

the probability of having horizontal mismatch between natives and immigrants after

controlling for observable characteristics, we estimate two binomial probit models.

XMISMVprob )_( (1)

XMISMHprob )_( (2)

where prob(V_MISM) and prob(H_MISM) denote the probability of being overeducated and

the probability of having horizontal mismatch respectively, is the standard normal

cumulative distribution function, X represents the set of observable characteristics and is the

coefficients’ vector.

The explanatory variables can be clustered in two groups. The first one is related to personal

characteristics of individuals as gender, age, immigrant condition (also distinguishing

immigrants from UE countries and from non‐UE countries), years of residence in the host

country, number of household members, whether there are children at home (13 years old or

less), level of education (ISCED3, ISCED4 and ISCED5&6), type or field of education (8

categories7) and whether the workers have followed any non‐formal education activity in the

last 12 months. As we focus our interest in immigrants and their process of assimilation, we

also include interactions between the variables related to their different origin and their years

of residence. The second group of characteristics is related to job characteristics as tenure in

the current firm (in years), type of contract (permanent or temporary), fulltime or part time

work, economic activity of the firm (5 categories) and firm size (we consider that 10 or less

workers is a small company and a company with more than 10 workers is a big company). We

also include country fixed‐effects.

7 Education: Teacher training and education science. / Humanities: Humanities, languages and arts. Foreign Languages. /Social Science: Social Science, business and law. / Science: Science, mathematics and computing. / Engineering: Engineering, manufacturing and construction. / Agriculture: Agriculture and veterinary. / Health: Health and welfare. / Services: Services.


10

To decompose the differences in the probability of having vertical (and horizontal) mismatch

between immigrants and natives, we then apply Yun’s (2004) methodology that is composed

by two steps. The first one consists in estimating equation (1) separately for immigrants and

natives:8,9

III XMISMVprob )_( (3)

NNN XMISMVprob )_( (4)

The second step consists in decomposing the mean difference between immigrants (I) and

natives (N) in the probability of having vertical (horizontal) mismatch as:

)()()()()_()_( IININIIINI XXXXMISMVprobMISMVprob (5)

E C

The component labeled E refers to the part of the differential due to differences in observable

characteristics. On the other hand, the C component refers to the part of the differential due

to differences in coefficients. The last component explains the differences in the probability of

being overeducated between immigrants and natives if both are characterized by the same

characteristics. The method also proposes a detailed decomposition to understand the unique

contribution of each predictor to each component of the difference. Yun (2004) also highlights

the need to take into account the normalization of dummy variables in order to solve the well‐

known problem in the detailed Oaxaca decomposition that it is not invariant to the choice of

the reference category when sets of dummy variables are used10. This correction is used in this

paper.

8 We apply the same methodology for the case of horizontal mismatch. 9 It is worth mentioning that in this kind of analysis it is not possible to include information on the years of residence as this characteristic is not shared also by natives. 10 See Yun (2004) for more details about Yun decomposition and the normalization of the dummy variables.


11

The marginal effects of the probability of being overeducated are shown in table 1. Models (1)

and (2) only include some personal characteristics as explanatory variables while in models (3)

to (5) additional controls are added sequentially.

TABLE 1

Results from model (1) clearly show that immigrants are more likely to be overeducated than

natives after controlling for observable characteristics (44.5%). However, the negative sign of

the variable years of residence indicates that the more are the years in the host country the

less is the probability to be overeducated. For each additional year of residence in the host

country, the probability of being overeducated is reduced by 3%. So, there seems to be an

assimilation process in the host country in terms of overeducation. In model (2) we introduce

two different dummies for immigrant workers distinguishing between immigrants from EU

countries and immigrants from non‐EU countries. In this case, we see that immigrants from

non‐EU countries are more likely to be overeducated than immigrants from EU countries.

Concerning the process of assimilation of both types of immigrants, the results for the

interactions between years of residence and immigrant dummies show that an additional year

of residence reduces the probability to be overeducated for immigrants from outside EU

countries more than for those coming from EU countries. In particular, the probability to be

overeducated for an immigrant from EU country is reduced 2.4% by year of residence in the

host country while this percentage is 3.5% for immigrants from countries outside EU. That is,

although immigrants from countries outside the EU have a higher probability to be

overeducated, their process of assimilation is faster than the one for immigrants from EU

countries. The results hold when additional controls are included in models (3) to (5).

The probability of having horizontal mismatch is shown in table 2. As before, models (1) and

(2) include only some controls while in models (3) to (5) additional explanatory variables are

included.

TABLE 2


12

Model (1) shows that immigrants are 15% more likely to have horizontal mismatch than

natives. It is worth noting that the incidence of horizontal mismatch on immigrants is much

lower than the incidence of overeducation (which corresponds to 44.5%) according to the

descriptive statistics. Regarding the years of residence in the host country, we can see that the

probability of having horizontal mismatch is only reduced by 1% for each additional year.

Results from model (2) show that immigrants from non‐UE countries are more likely to have

horizontal mismatch than natives. However, this effect is no longer statistically significant for

immigrants from EU countries when compared to natives. Moreover, the interactions between

years of residence and both types of immigrants are not significant. When additional variables

are included in models (3) to (5), the higher probability of horizontal mismatch of immigrants

from non‐EU countries is no longer significant when compared to natives. This means that

differences in the characteristics of natives and immigrants explain the raw difference in the

probability of having horizontal mismatch.

Given that there are no differences statistically significant in the probability of having

horizontal mismatch between immigrants and natives, we only apply the Yun (2004)

decomposition in the case of vertical mismatch. This decomposition allows us to identify which

factors influence in the discrepancies in the probability of being overeducated between

immigrants and natives. In particular, the method decompose whether the differences are due

to different observable characteristics (worse endowment of human capital or worse job

characteristics), or whether the remuneration of those characteristics is worse for immigrants

than for natives. Table 3 shows the aggregated results of Yun’s (2004) decomposition11. From

this table we can see that the differences in the probability of being overeducated between

both types of immigrants and natives are statistically significant and consistent with the

differences in the percentages of overeducation between groups observed in figure 2. In

particular, we obtain that this difference is around 6%, although it is around 10% when

immigrants from non‐EU countries are compared to natives. In both cases, immigrants

experience the higher probability of being overeducated, but the causes of these differences

are not the same in both cases. In the case of the difference in the probability of being

overeducated between immigrants from EU countries and natives, we can see that the 61% of

this difference is explained by differences in characteristics. So, immigrants from EU countries

have higher probability of being overeducated because they have worst observable

characteristics than natives. The 39% of the difference is due to differences in coefficients, but 11 The results of the detailed decomposition are shown in Table A.2. in the Annex.


13

is not statistically significant. That is, immigrants from EU and natives with the same

endowments are equally remunerated. Concerning the difference in the probability of being

overeducated between immigrants from non‐EU countries and natives, the 81% of this

difference can be explained by differences in coefficients (is statistically significant). That is,

immigrants from non‐EU countries are not remunerate at the same way than natives, although

both are characterized by the same endowments.

5. FINAL REMARKS

In this paper, we have analysed differences in skill mismatches between immigrants and

natives in EU countries. Using microdata from the Adult Education Survey (AES), we have

analysed the incidence of different types of skill mismatches (vertical and horizontal) among

native and immigrant workers.

Our results show that there is no significant difference in the probability of having horizontal

mismatch between natives and immigrants once individual characteristics are controlled for.

However, we found that immigrants are more likely to be overeducated than natives, and that

this effect is higher for immigrants from non‐EU countries than for those from other EU

countries, although the pace of the assimilation process in the host country is faster for the

first group. Applying Yun’s (2004) decomposition, we also found that immigrants from the EU

have a higher probability of being overeducated than natives because they are characterized

by worse observable characteristics which influence positively the probability of

overeducation, whereas results for immigrants from non‐EU countries suggest the opposite:

the gap is explained by differences in the remuneration of observable characteristics. This

result points out that immigrants from non‐UE countries have a limited transferability of their

human capital that pushes their situation of overeducation in the host country.

To sum up, our results confirm that immigrants experience a higher overeducation penalty

than natives due to the imperfect transferability of the human capital acquired in their origin

countries. However, immigrants accumulate knowledge and experience in the host country

that adapt to the local labour market, thus facilitating an assimilation process that reduces the

intensity of overeducation. The pace of assimilation however is notably slow for immigrants.

Therefore there is a certain risk that immigrants from outside the European Union remain


14

permanently trapped in bad jobs, regardless of their levels of education. Taking into account

the wage consequences of overeducation, this last result implies that the wage gap between

native and immigrants will not disappear after several years of residence in the host country.

Policy actions should focus on three different aspects: first, incorporating in the migration

policy formal criteria related to educational levels and to the match with the current needs in

the labour market (i.e, like the Australian points system); second, trying to design a system of

assessment and recognition of foreign‐acquired educational degrees in order to give an

appropriate signal to the labour market and, third, providing publicy‐provided informal

training to recently arrived immigrants with appropriate skills in order to improve the

transferability of their skills to the new labour market.


15

6. REFERENCES

Aleksynska, M. & Tritah, A. (2011). Occupation‐Education Mismatch of Immigrant Workers in

Europe: Context and Policies. CEPII WP 2011‐16.

Baker, M. & Benjamin, D. (1994). The Performance of Immigrants in the Canadian Labor

Market, Journal of Labor Economics, vol. 12(3), pp. 369‐405.

Bell, B. (1997). The Performance of Immigrants in the United Kingdom: Evidence from the GHS.

Economic Journal, vol. 107(441), pp. 333‐344.

Card, D. (1999). Causal Effect of Education on Earnings. In: Ashenfelter O, Card D (dir.),

Handbook of Labor Economics, vol. 3, Elsevier Science: Amsterdam; 1801‐1863.

Chiswick, B. R. (1978). The Effect of Americanization on the Earnings of Foreign‐born Men.

Journal of Political Economy, vol. 86(5): 897‐921.

Chiswick, B. R. & Miller, P. (1985). Immigrant Generation and Income in Australia. Economic

Record, vol. 61(173), pp. 540‐553.

Chiswick, B. R. & Miller, P. (1995). The endogeneity between language and earnings: an

international analysis. Journal of Labor Economics, vol. 13(2), pp. 246‐288.

Chiswick, B. R. & Miller, P. (2007). The International Transferability of Immigrants’ Human

Capital Skills. IZA Discussion paper n. 2670, March 2007, 27 p.

Chiswick, B. & Miller, P. (2008). Why is the payoff to schooling smaller for immigrants?. Labour

Economics, vol. 15, pp. 1317–1340.

Chiswick, B. & Miller, P. (2009). The International Transferability of Immigrants’ Human Capital

Skills. Economics of Education Review, vol. 28 (2), pp. 162‐169.

Chiswick, B. & Miller, P. (2010). The Effects of Educational‐Occupational Mismatch on

Immigrant Earnings in Australia, with International Comparisons. International Migration

Review, vol. 44(4), pp. 869–898.

Constant, A. & Massey, D. (2003). Self‐selection, earnings, and out‐migration: A longitudinal

study of immigrants to Germany. Journal of Population Economics, vol. 16, pp. 631‐653.

Dell’Aringa. C. & Pagani, L. (2010). Labour Market Assimilation and Over Education: The Case of

Immigrant Workers in Italy. Quaderni dell’Istituto di Economia dell’Impresa e del Lavoro,

57.


16

Duleep, H. & Regets, M. (1997). The Decline in Immigrant Entry Earnings: Less Transferable

Skills or Lower Ability?. The Quarterly Review of Economics and Finance, vol. 37, Special

Issue, pp. 189‐208.

Duncan, G. & Hoffman, S. (1981). The Economic Value of Surplus Education. Economics of

Education Review vol. 1 (1), pp. 75‐86.

Dustman, C. & Glitz, A. (2011). Migration and Education. in Handbook of the Economics of

Education, vol. 4, pp. 327‐439.

Freeman, R. B. (1976). The Overeducated American. London: Academic Press.

Friedberg, R. (2000). You Can’t Take It with You? Immigrant Assimilation and the Portability of

Human Capital. Journal of Labor Economics, vol. 18, n. 2, pp. 221‐251.

Green, C., Kler, P. & Leeves, G. (2007). Immigrant overeducation: Evidence from recent arrivals

to Australia. Economics of Education Review 26, pp. 420‐432.

Hartog, J. (2000). Over‐education and earnings: where are we, where should we go?.

Economics of Education Review, vol. 19, pp. 131–147.

Kiker, B.F., Santos, M.C. & Mendes de Oliveira, M. (1997). Overeducation and Undereducation:

Evidence for Portugal. Economics of Education Review, vol. 16 (2), pp. 111‐125.

Kler, P. (2006). Overeducation among tertiary educated immigrants to Australia: A longitudinal

study. Labour Economics Research Group, University of Queensland, Discussion Paper n.

9, January 2006, 39 p.

Leuven, E. & Oosterbeck, H, (2011). Overeducation and Mismatch in the Labor Market in

Handbook of the Economics of Education, vol. 4, pp. 283‐326.

Lindley, J. & Lenton, P. (2006). The Overeducation of UK Immigrants: Evidence from the Labour

Force Survey. Sheffield Economic Research Paper Series n. 2006001, 20 p.

Longva, P. & Raaum, O. (2003). Earnings assimilation of immigrants in Norway – A reappraisal.

Journal of Population Economics, vol. 16, pp. 177‐193.

Mcguinness, S. (2006), Overeducation in the labour market. Journal of Economic Surveys, Vol.

20 (3), pp. 387‐418.

Nielsen, Ch. P. (2007). Immigrant Overeducation: Evidence from Denmark. World Bank Policy

Research Working Paper 4234, may 2007, 54 p.

OECD (2007). International Migration Outlook, Annual Report 2007. OECD, Paris.

Piracha, M. & Vadean, F. (2012). Migrant Educational Mismatch and the Labour Market. IZA DP

6414.


17

Poot, J. & Stillman, S. (2010). The Importance of Heterogeneity When Examining Immigrant

Education‐Occupation Mismatch: Evidence from New Zealand. IZA DP 5211.

Psacharopoulos, G. & Patrinos, H. A. (2004). Returns to investment in education: A further

update. Education Economics, vol. 12(2), pp. 111‐134.

Robst, J. (2007). Education and job match: The relatedness of college major and work.

Economics of Education Review, vol. 26(4), pp. 397‐407.

Rumberger, R. (1981). Overeducation in the US Labor Market. Praeger, New York.

Rubb, S. (2003). Overeducation in the labor market: A comment and re‐analysis of a meta‐

analysis. Economics of Education Review, vol. 22 (6), pp. 621‐629.

Sanromá, E., Ramos, R. & Simón, H. (2008). The Portability of Human Capital and Immigrant

Assimilation: Evidence for Spain. IZA Discussion Paper No. 3649

Schmidt, C. (1992). Country of origin differences in the earnings of German immigrants.

Discussion Paper 92‐29, University of Munich.

Shields, M. & Wheatley Price, S. (1998), The earnings of male immigrants in England: evidence

from the quarterly LFS, Applied Economics, vol. 30, pp. 1157‐1168.

Verdugo, R. & Verdugo, N. (1989). The impact of surplus schooling on earnings. Journal of

Human Resources, vol. 24 (4), pp. 629‐643.

Wolbers, M. (2003). Job Mismatches and their Labour‐Market Effects among School‐Leavers in

Europe. European Sociological Review, vol. 19 (3), pp. 249‐266.

Yun, M. (2004). Decomposing differences in the first moment. Economics Letters, vol. 82(2),

pp. 275‐280.


18

7. FIGURES AND TABLES

Figure 1. Proportion of immigrant’ population in total population (average 2009‐2011)

Source: Eurostat

Figure 2. Percentage of vertical mismatch Figure 3. Percentage of horizontal mismatch

Data: AES 2007 Data: AES 2007


19

Figure 4. Percentage of immigrants with vertical mismatch by years of residence in the host

country

Data: AES 2007

Figure 5. Percentage of immigrants with horizontal mismatch by years of residence in the

host country

Data: AES 2007


20

Table 1: Marginal effects of the probability to be overeducated

VARIABLES (1) (2) (3) (4) (5)

Immigrant 0.445*** [0.0524] Immig. UE 0.352*** 0.348*** 0.294*** 0.290*** [0.0865] [0.0859] [0.0867] [0.0862]Immig. no‐UE 0.515*** 0.515*** 0.461*** 0.463*** [0.0631] [0.0625] [0.0667] [0.0663]Male 0.00286 0.00279 ‐0.0133 0.00307 0.00322 [0.00776] [0.00776] [0.00930] [0.00961] [0.00961]Age ‐0.00413*** ‐0.00413*** ‐0.00385*** ‐0.00197*** ‐0.00197*** [0.000398] [0.000398] [0.000399] [0.000479] [0.000479]Years of residence ‐0.0304*** [0.00460] Years of residence x immig. UE ‐0.0239*** ‐0.0241*** ‐0.0212*** ‐0.0206*** [0.00711] [0.00702] [0.00697] [0.00693]Years of residence x immig. no‐UE ‐0.0354*** ‐0.0354*** ‐0.0319*** ‐0.0316*** [0.00606] [0.00597] [0.00586] [0.00584]Household size (nº of people) 0.00972** 0.00972** 0.00932** 0.00856* 0.00731 [0.00469] [0.00470] [0.00466] [0.00461] [0.00463]Children at home ‐0.00413 ‐0.00427 ‐0.00383 ‐0.00528 ‐0.00647 [0.00818] [0.00818] [0.00815] [0.00822] [0.00824]Educational level (ref. ISCED3)

ISCED4 0.696*** 0.696*** 0.703*** 0.705*** 0.706*** [0.0114] [0.0114] [0.0112] [0.0111] [0.0111]ISCED5&6 0.134*** 0.135*** 0.157*** 0.166*** 0.169*** [0.00972] [0.00972] [0.0104] [0.0106] [0.0106]

Non formal education ‐0.0399*** ‐0.0396*** ‐0.0327*** ‐0.0203** ‐0.0209*** [0.00820] [0.00819] [0.00811] [0.00812] [0.00812]Field of education (ref. education)

Humanities 0.229*** 0.203*** 0.206*** [0.0320] [0.0321] [0.0322]Social science 0.194*** 0.158*** 0.159*** [0.0254] [0.0259] [0.0260]Science 0.135*** 0.105*** 0.108*** [0.0319] [0.0315] [0.0317]Engineering 0.193*** 0.156*** 0.156*** [0.0259] [0.0264] [0.0264]Agriculture 0.304*** 0.253*** 0.249*** [0.0389] [0.0410] [0.0411]Health 0.127*** 0.121*** 0.121*** [0.0283] [0.0282] [0.0282]Services 0.282*** 0.244*** 0.245***

[0.0330] [0.0340] [0.0340]Economic activity (ref. industry)

Agriculture 0.0113 0.00761 [0.0286] [0.0284]Construction ‐0.00911 ‐0.00897 [0.0174] [0.0175]Services ‐0.00995 ‐0.00737 [0.0113] [0.0114]No sale services ‐0.0540*** ‐0.0527*** [0.0121] [0.0121]

Tenure ‐0.00295*** ‐0.00298*** [0.000519] [0.000518]Fulltime job ‐0.0502*** ‐0.0502*** [0.0120] [0.0120]Temporary contract 0.0305** 0.0306** [0.0135] [0.0134]Big company (more than 10 workers) ‐0.0444*** ‐0.0425*** [0.0100] [0.0101]Urban Size No No No No YesCountry F.E. Yes Yes Yes Yes YesObservations 30901 30901 30901 30901 30901

Note: Robust standard errors are reported between brackets. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at

the 1% level.


21

Table 2: Marginal effects of the probability to present horizontal mismatch

VARIABLES (1) (2) (3) (4) (5)

Immigrant 0.151*** [0.0579] Immig. UE 0.130 0.0467 0.0433 0.0434 [0.0835] [0.0826] [0.0862] [0.0863]Immig. no‐UE 0.161** 0.140 0.110 0.110 [0.0781] [0.0889] [0.0945] [0.0945]Male ‐0.0545*** ‐0.0547*** ‐0.0413*** ‐0.0103 ‐0.0103 [0.00934] [0.00934] [0.0146] [0.0147] [0.0147]Age 0.000666 0.000681 0.00133** 0.00441*** 0.00441*** [0.000503] [0.000503] [0.000608] [0.000726] [0.000727]Years of residence ‐0.0100* [0.00577] Years of residence x immig. UE ‐0.0118 ‐0.00704 ‐0.00819 ‐0.00820 [0.00858] [0.00926] [0.00963] [0.00963]Years of residence x immig. no‐UE ‐0.00878 ‐0.00849 ‐0.00743 ‐0.00744 [0.00771] [0.00889] [0.00937] [0.00937]Household size (nº of people) ‐0.00609 ‐0.00636 ‐0.000252 0.00502 0.00504 [0.00595] [0.00597] [0.00737] [0.00748] [0.00755]Children at home ‐0.0125 ‐0.0127 ‐0.0127 ‐0.0118 ‐0.0118 [0.0100] [0.0100] [0.0121] [0.0122] [0.0122]Educational level (ref. ISCED3)

ISCED4 ‐0.0136 ‐0.0137 ‐0.0318 ‐0.0445 ‐0.0445 [0.0231] [0.0231] [0.0275] [0.0274] [0.0274]ISCED5&6 0.0227** 0.0228** ‐0.0270** ‐0.0416*** ‐0.0416*** [0.0104] [0.0104] [0.0132] [0.0134] [0.0135]

Non formal education 0.0243** 0.0251*** 0.0234* 0.0194 0.0195 [0.00972] [0.00971] [0.0120] [0.0120] [0.0120]

Fieldofeducation(ref.education)

Humanities 0.598*** 0.605*** 0.605*** [0.0113] [0.0107] [0.0107]Social science ‐0.205*** ‐0.209*** ‐0.209*** [0.0213] [0.0221] [0.0221]Science 0.624*** 0.629*** 0.629*** [0.00707] [0.00714] [0.00714]Engineering ‐0.101*** ‐0.0692** ‐0.0692** [0.0247] [0.0269] [0.0269]Agriculture 0.482*** 0.496*** 0.496*** [0.0201] [0.0190] [0.0190]Health 0.0616** 0.0518** 0.0518** [0.0251] [0.0253] [0.0253]Services 0.438*** 0.427*** 0.427***

[0.0214] [0.0232] [0.0232]Economic activity (ref. industry)

Agriculture 0.0229 0.0231 [0.0545] [0.0546]Construction ‐0.190*** ‐0.190*** [0.0206] [0.0206]Services 0.104*** 0.104*** [0.0180] [0.0181]No sale services 0.103*** 0.103*** [0.0192] [0.0192]

Tenure ‐0.00612*** ‐0.00612*** [0.000715] [0.000714]Fulltime job ‐0.00506 ‐0.00507 [0.0171] [0.0171]Temporary contract 0.0125 0.0125 [0.0202] [0.0202]Big company (more than 10 workers) 0.000894 0.000854 [0.0139] [0.0140]Urban Size No No No No YesCountry F.E. Yes Yes Yes Yes Yes


22

Observations 30901 30901 30901 30901 30901

Note: Robust standard errors are reported between brackets. * Significant at the 10% level. ** Significant at the 5% level. *** Significant

at the 1% level.


23

Table 3: General decomposition of the differences in the probability of being overeducated between immigrants and natives

Immigrants from EU vs. Natives Immigrants from non‐EU vs. Natives

Diff. in characteristics 0.0364*** 0.0188 (61%) (19%)Diff. in coefficients 0.0233 0.0816*** (39%) (81%) Total 0.0597*** 0.100*** Note: Percentages of the contribution are reported between parentheses. * Significant at the 10% level ** Significant at the 5% level. *** Significant at the 1% level.


24

8. Annex

Table A.1. Descriptive statistics

Natives Immigrant from EU Immigrant from outside EU

Variable Mean Std. Dev Mean Std. Dev Mean Std. Dev

Vertical mismatch 0.2489 0.4324 0.3101 0.4628 0.3510 0.4774

Horizontal mismatch 0.3904 0.4878 0.3970 0.4895 0.4521 0.4979

Male 0.5212 0.4996 0.5813 0.4936 0.6064 0.4887

Female 0.4788 0.4996 0.4187 0.4936 0.3936 0.4887

Age 42.0981 9.7277 42.0288 9.5370 41.3213 9.2157

Years of residence 0.0000 0.0000 9.5300 2.8557 9.5134 2.6015

Household size (nº of people) 2.1413 0.8149 2.0994 0.7988 2.2415 0.8786

Children at home 0.3780 0.4849 0.4323 0.4957 0.4590 0.4985

No children at home 0.6160 0.4864 0.5627 0.4963 0.5278 0.4994

Education level ISCED3 0.5391 0.4985 0.5303 0.4994 0.5682 0.4955

Education level ISCED4 0.0711 0.2569 0.0495 0.2170 0.0624 0.2420

Education level ISCED5&6 0.3899 0.4877 0.4202 0.4939 0.3694 0.4828

Non‐formal education (NFE) 0.5494 0.4976 0.5281 0.4995 0.3802 0.4856

No NFE 0.4506 0.4976 0.4719 0.4995 0.6198 0.4856

Field of education:

Education 0.0561 0.2300 0.0372 0.1893 0.0327 0.1779

Humanities 0.0554 0.2288 0.0949 0.2932 0.0575 0.2328

Social science 0.2912 0.4543 0.1868 0.3900 0.2280 0.4197

Science 0.0518 0.2216 0.0597 0.2370 0.0752 0.2639

Engineering 0.3404 0.4739 0.4667 0.4992 0.4062 0.4913

Agriculture 0.0265 0.1606 0.0178 0.1324 0.0243 0.1540

Health 0.1072 0.3093 0.0676 0.2511 0.0776 0.2676

Services 0.0715 0.2577 0.0693 0.2541 0.0984 0.2980

Economic activity:

Agriculture 0.0124 0.1109 0.0049 0.0696 0.0099 0.0989

Industry 0.2301 0.4209 0.2225 0.4162 0.2669 0.4425

Construction 0.0616 0.2404 0.0982 0.2977 0.0873 0.2824

Services 0.3191 0.4661 0.4061 0.4914 0.3604 0.4803

No sale services 0.3768 0.4846 0.2684 0.4434 0.2755 0.4469

Tenure 13.0985 10.1193 9.9585 8.3261 8.7355 7.9138

Full time job 0.8220 0.3825 0.8140 0.3893 0.8374 0.3691

Part time job 0.1780 0.3825 0.1860 0.3893 0.1626 0.3691

Temporary contract 0.0784 0.2688 0.1401 0.3473 0.1795 0.3839

Permanent contract 0.8981 0.3025 0.8115 0.3914 0.8173 0.3865

Firm size

Big company 0.7906 0.4069 0.7784 0.4156 0.7499 0.4332

Small company 0.2094 0.4069 0.2216 0.4156 0.2501 0.4332


25

Observations 28409 28409 894 894 1598 1598


26

Table A.2. Detailed Yun decomposition of the probability of being overeducated

between immigrants and natives (continues)

Immigrants from EU countries vs. natives

Immigrants from non‐EU countries vs. natives

VARIABLES E C E C

Total dif. between groups 0.0597*** 0.100*** [0.0192] [0.0141] Total 0.0364*** 0.0233 0.0188 0.0816*** [0.0119] [0.0193] [0.0135] [0.0178] Male ‐0.00393*** 0.636 ‐0.00317 ‐0.0244* [0.00147] [11.19] [0.00234] [0.0141] Female ‐0.00393*** ‐0.584 ‐0.00317 0.0224* [0.00147] [10.28] [0.00234] [0.0129] Age ‐0.000475*** ‐6.817 ‐0.00319 0.336*** [0.000173] [120.6] [0.00206] [0.125] Isced3 0.00224*** 0.215 ‐0.0154*** ‐0.112*** [0.000369] [3.808] [0.00525] [0.0269] Isced4 ‐0.00844*** ‐0.0758 ‐0.00645*** 0.0207*** [0.00143] [1.336] [0.00230] [0.00559] Isced5_6 ‐0.00409*** 0.261 0.00442** ‐0.0325* [0.00120] [4.586] [0.00189] [0.0184] NFE ‐7.76e‐05 ‐0.138 0.00147 0.00384 [0.000421] [2.467] [0.00375] [0.0132] No NFE ‐7.76e‐05 0.113 0.00147 ‐0.00315 [0.000421] [2.023] [0.00375] [0.0109] Household size ‐0.00112 ‐0.822 0.00362 0.0655 [0.000974] [14.66] [0.00222] [0.0530] Children at home 0.000785 ‐0.117 ‐0.00104 ‐0.00300 [0.00104] [2.082] [0.00183] [0.00861] No children at home 0.000770 0.191 ‐0.00114 0.00489 [0.00102] [3.393] [0.00200] [0.0140] Field of education:

Education 0.00223 ‐0.00719 0.00346 0.00167 [0.00177] [0.156] [0.00220] [0.00527] Humanities 0.00269 ‐0.0307 4.94e‐05 ‐0.00150 [0.00262] [0.545] [0.000125] [0.00356] Social Science 0.00633 0.368 ‐0.00115 ‐8.90e‐05 [0.00504] [6.442] [0.00298] [0.0144] Science 0.000179 ‐0.0587 0.00135 0.00631 [0.000670] [1.046] [0.00164] [0.00387] Engineering ‐0.00771 0.442 ‐0.00167 ‐0.0161 [0.00653] [7.848] [0.00302] [0.0160] Agriculture ‐0.00162 ‐0.0580 ‐0.000363 0.00226 [0.00101] [1.023] [0.000236] [0.00266] Health 0.00451** 0.171 0.00403 ‐0.0118 [0.00228] [3.030] [0.00246] [0.00826] Services ‐0.000168 ‐0.0111 0.00117 ‐0.00373

[0.000119] [0.203] [0.00195] [0.00538]

Note: Robust standard errors are reported between brackets. * Significant at the 10% level. ** Significant at the 5% level.

*** Significant at the 1% level.


27


between immigrants and natives (continuation)



VARIABLES E C E C

Economic activity: Agriculture ‐0.00136 ‐0.0340 1.45e‐07 ‐0.000364 [0.000873] [0.600] [0.000277] [0.00142] Industry ‐3.86e‐05 0.0245 0.00104 0.00295 [0.000369] [0.467] [0.00186] [0.0125] Construction 0.00458** ‐0.147 0.00265 0.00779** [0.00227] [2.598] [0.00173] [0.00387] Services ‐0.00817** 0.559 0.00109 0.00484 [0.00370] [9.904] [0.00177] [0.0141] No sale services 0.0234*** 1.229 0.0160** ‐0.0472**

[0.00595] [21.67] [0.00738] [0.0223] Tenure 0.0224*** 1.070 0.0339*** ‐0.0599 [0.00858] [18.81] [0.0113] [0.0441] Fulltime job 4.52e‐05 ‐0.231 ‐0.000616 ‐0.00735 [0.000212] [4.119] [0.000560] [0.0274] Part time job 4.52e‐05 0.0501 ‐0.000616 0.00159 [0.000212] [0.892] [0.000560] [0.00593] Temporary contract ‐6.54e‐05 0.0124 0.00734* 0.00492** [0.00174] [0.224] [0.00378] [0.00241] Permanent contract ‐9.18e‐05 ‐0.142 0.00587* ‐0.0564** [0.00245] [2.564] [0.00302] [0.0277] Big company 0.000441 0.232 ‐8.29e‐05 0.0234 [0.000308] [4.158] [0.00108] [0.0225] Small company 0.000441 ‐0.0614 ‐8.29e‐05 ‐0.00620 [0.000308] [1.101] [0.00108] [0.00596] Urban size:

Big degree urb. ‐0.000326 ‐0.0728 ‐0.00522 ‐0.00445 [0.00429] [1.282] [0.00672] [0.0140] Medium degree urb. 0.00277 0.136 0.00382 ‐0.0188 [0.00349] [2.395] [0.00301] [0.0133] Small degree urb. ‐0.000750 ‐0.0552 ‐0.01000 0.0153

[0.00101] [0.986] [0.00712] [0.0104]




28


between immigrants and natives (end)



VARIABLES E C E C

Countries: Austria ‐0.000571 0.0396 0.00138** 0.00935*** [0.000564] [0.703] [0.000588] [0.00262] Belgium 9.70e‐05 ‐0.0162 ‐0.00171 0.00449 [0.000979] [0.281] [0.00167] [0.00313] Cyprus 0.000244*** ‐0.00346 1.03e‐05** 0.000445* [9.48e‐05] [0.0605] [4.71e‐06] [0.000247] Czech Republic 0.000377 0.0281 ‐0.00853 0.00814 [0.00205] [0.503] [0.00766] [0.00712] Germany ‐0.00208 0.272 ‐0.0130* ‐0.0558** [0.00291] [4.731] [0.00727] [0.0251] Denmark ‐0.000356 0.0238 ‐0.00339 0.00250 [0.00140] [0.426] [0.00458] [0.00524] Estonia 0.000799* 0.0118 ‐0.00148* ‐0.000129 [0.000412] [0.209] [0.000756] [0.000318] Spain 0.00504*** ‐0.434 0.0125*** 0.0299*** [0.000979] [7.670] [0.00471] [0.00796] France ‐0.00491 0.0435 ‐0.00468 ‐0.00372 [0.00491] [0.778] [0.00346] [0.0139] Lithuania 0.00236 ‐0.0154 0.000397*** ‐0.0108*** [0.00312] [0.288] [0.000150] [0.00276] Latvia 0.000728 0.0172 ‐0.00317** ‐0.00256** [0.000695] [0.304] [0.00136] [0.001000] Portugal 0.00192** ‐0.0214 0.00103* 0.000615 [0.000779] [0.376] [0.000606] [0.00101] Sweden 0.00137 0.0181 6.56e‐06** 0.00282

[0.00120] [0.329] [2.85e‐06] [0.00329] Constant 3.814 ‐0.0186 [67.54] [0.127] Observations 29303 29303 30007 30007



Documents

Skill mismatches in the EU: Immigrants vs. Natives Sandra ... · effective work experience in Denmark. Thus, they successfully assimilate. As for the returns of years of overeducation,